Machine Learning Engineer

I am partnering with an innovative Deeptech Biotech company based in Paris, France who are harnessing the power of Artificial Intelligence and multimodal data to transform the Drug Discovery process in Immunology.

Position: Machine Learning Engineer

Location: Paris, France (3 days a week onsite / 2 days remote working)

About the Role

You will lead the design and deployment of scalable machine learning systems tailored for complex biomedical data. Your focus will be on developing infrastructure and pipelines that support cutting-edge foundation models, enabling breakthroughs in therapeutic discovery and healthcare innovation.

Key Responsibilities

• Architect, build, and optimize transformer-based models designed for high-dimensional biomedical datasets—including transcriptomics, histology, and rich clinical records—for tasks such as pre-training, fine-tuning, and benchmarking.
• Develop and maintain robust MLOps workflows, covering data preprocessing, model training, evaluation, deployment, monitoring, versioning, and scheduled retraining.
• Transform large-scale biomedical data into efficient, deep-learning-compatible formats to support high-throughput model training.
• Collaborate closely with domain experts—including computational biologists and AI researchers—to translate scientific insights into performant, production-ready ML systems.
• Champion engineering best practices, fostering reproducibility, high code quality, and continuous improvement within the ML infrastructure.

Ideal Candidate

• Master’s or PhD in Machine Learning, Computer Science, Data Science, or a related technical field.
• 3+ years of hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow, JAX).
• Proficiency in Python and a strong background in building production-grade ML systems—familiarity with Docker, Kubernetes, and cloud environments (AWS, GCP, Azure) is essential.
• Experience with MLOps tooling (e.g., Kubeflow, MLflow, W&B, or similar), plus solid knowledge of CI/CD workflows and infrastructure-as-code practices.
• Skilled in GPU computation, distributed training, and model performance optimization techniques.

Desirable Skills

• Prior experience with biomedical or healthcare datasets—especially modalities such as transcriptomics or histology.
• Background in working with large-scale foundation models, especially in domains like NLP or multimodal data.
• Comfortable in fast-paced, ambiguous startup-like environments—solution-driven and proactive.

Following your application, Jay Robins, a specialist AI recruiter will discuss the opportunity with you in detail.

He will be more than happy to answer any questions relating to the industry and the potential for your career growth.

The conversation can also progress further to discussing other opportunities, which are also available right now or will be imminently becoming available.

This position has been highly popular, and it is likely that it will close prematurely. We recommend applying as soon as possible to avoid disappointment.